Optimization of dynamic mobile robot path planning based on evolutionary methods

TL;DR

This paper compares evolutionary algorithms like PSO, GA, and PS for optimizing dynamic mobile robot path planning, demonstrating PSO's superior convergence and efficiency in obstacle-rich environments.

Contribution

It introduces an application of PSO, GA, and PS to dynamic path planning, highlighting PSO's advantages in convergence and speed over other methods.

Findings

PSO outperforms GA and PS in convergence and objective minimization.

PS has lower computation time in dynamic environments.

All algorithms successfully navigate obstacle-rich paths.

Abstract

This paper presents evolutionary methods for optimization in dynamic mobile robot path planning. In dynamic mobile path planning, the goal is to find an optimal feasible path from starting point to target point with various obstacles, as well as smoothness and safety in the proposed path. Pattern search (PS) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to find an optimal path for mobile robots to reach to target point with obstacle avoidance. For showing the success of the proposed method, first they are applied to two different paths with a dynamic environment in obstacles. The first results show that the PSO algorithms are converged and minimize the objective function better that the others, while PS has the lower time compared to other algorithms in the initial and modified environment. The second test path is in the z-type environment that we compare the mentioned algorithms on it. Also in this environment, the same result is repeated.